Refactor into modular app structure

Split monolithic files into focused modules:
- app/core: settings, logging, lifecycle
- app/signaling: websocket server, ICE parser, message models
- app/webrtc: peer session, video receiver, frame source
- app/vision: pose landmarker wrapper, model config, pose types
- app/exercises/dead_bug: detector, metrics, rules, state machine, types
- app/rendering: skeleton renderer, status overlay, window display
- app/audio: rep announcer
- app/diagnostics: perf timer, crash handler
- configs: environment-based settings
- tests: unit tests for rules, state machine, ICE parser
- run.py: entry point
This commit is contained in:
2026-06-10 10:14:43 +08:00
parent 8b878cb9e5
commit 4485cbf702
44 changed files with 1230 additions and 648 deletions
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from __future__ import annotations
import threading
import time
import cv2
import mediapipe as mp
import numpy as np
from loguru import logger
from app.exercises.dead_bug.metrics import calculate_metrics
from app.exercises.dead_bug.rules import has_required_visibility
from app.exercises.dead_bug.state_machine import DeadBugStateMachine
from app.exercises.dead_bug.types import DeadBugMetrics, DeadBugPhase, DeadBugResult, Point
from app.rendering.overlay_renderer import draw_status_overlay
from app.rendering.skeleton_renderer import draw_landmarks
from app.vision.frame_utils import bgr_to_rgba
from app.vision.pose_landmarker import PoseLandmarkerWrapper
from app.vision.pose_types import (
LEFT_ANKLE,
LEFT_ELBOW,
LEFT_HIP,
LEFT_KNEE,
LEFT_SHOULDER,
LEFT_WRIST,
REQUIRED_LANDMARKS,
RIGHT_ANKLE,
RIGHT_ELBOW,
RIGHT_HIP,
RIGHT_KNEE,
RIGHT_SHOULDER,
RIGHT_WRIST,
)
class DeadBugDetector:
def __init__(
self,
*,
model_path: str | None = None,
visibility_threshold: float = 0.45,
extension_confirm_frames: int = 4,
reset_confirm_frames: int = 3,
prefer_gpu: bool = True,
) -> None:
self.visibility_threshold = visibility_threshold
self._latest_result = None
self._result_lock = threading.Lock()
self._result_event = threading.Event()
self._inflight = False
self._inflight_started_at = 0.0
def on_result(pose_result, _image, _timestamp_ms):
with self._result_lock:
self._latest_result = pose_result
self._inflight = False
self._inflight_started_at = 0.0
self._result_event.set()
self._landmarker = PoseLandmarkerWrapper(
model_path=model_path,
prefer_gpu=prefer_gpu,
result_callback=on_result,
)
self._state = DeadBugStateMachine(
extension_confirm_frames=extension_confirm_frames,
reset_confirm_frames=reset_confirm_frames,
)
self._last_timestamp_ms = -1
def close(self) -> None:
self._landmarker.close()
def process_frame(self, bgr_frame: np.ndarray, timestamp_ms: int) -> tuple[np.ndarray, DeadBugResult]:
timestamp_ms = self._normalize_timestamp(timestamp_ms)
with self._result_lock:
if self._inflight and time.monotonic() - self._inflight_started_at > 0.5:
logger.warning("MediaPipe detect_async timed out; allowing next frame submission")
self._inflight = False
self._inflight_started_at = 0.0
should_submit = not self._inflight
if should_submit:
self._inflight = True
self._inflight_started_at = time.monotonic()
if should_submit:
rgba_frame = bgr_to_rgba(bgr_frame)
mp_image = mp.Image(image_format=mp.ImageFormat.SRGBA, data=rgba_frame)
self._result_event.clear()
try:
self._landmarker.detect_async(mp_image, timestamp_ms)
except Exception:
with self._result_lock:
self._inflight = False
self._inflight_started_at = 0.0
raise
self._result_event.wait(timeout=0.08)
with self._result_lock:
pose_result = self._latest_result
annotated = bgr_frame.copy()
if pose_result is None or not pose_result.pose_landmarks:
result = DeadBugResult(
rep_count=self._state.rep_count,
phase=DeadBugPhase.NO_POSE,
side=self._state.active_side,
is_standard=False,
feedback=["No full body detected"],
metrics=None,
)
draw_status_overlay(annotated, result)
return annotated, result
landmarks = [Point(lm.x, lm.y, lm.z, getattr(lm, "visibility", 1.0)) for lm in pose_result.pose_landmarks[0]]
draw_landmarks(annotated, landmarks, REQUIRED_LANDMARKS, visibility_threshold=self.visibility_threshold)
if not has_required_visibility(landmarks, REQUIRED_LANDMARKS, self.visibility_threshold):
result = DeadBugResult(
rep_count=self._state.rep_count,
phase=DeadBugPhase.NO_POSE,
side=self._state.active_side,
is_standard=False,
feedback=["Keep shoulders, elbows, wrists, hips, knees, ankles visible"],
metrics=None,
)
draw_status_overlay(annotated, result)
return annotated, result
raw = calculate_metrics(
landmarks,
left_shoulder=LEFT_SHOULDER,
right_shoulder=RIGHT_SHOULDER,
left_elbow=LEFT_ELBOW,
right_elbow=RIGHT_ELBOW,
left_wrist=LEFT_WRIST,
right_wrist=RIGHT_WRIST,
left_hip=LEFT_HIP,
right_hip=RIGHT_HIP,
left_knee=LEFT_KNEE,
right_knee=RIGHT_KNEE,
left_ankle=LEFT_ANKLE,
right_ankle=RIGHT_ANKLE,
visibility_threshold=self.visibility_threshold,
)
metrics = DeadBugMetrics(
left_arm_extended=raw["left_arm_extended"],
right_arm_extended=raw["right_arm_extended"],
left_leg_extended=raw["left_leg_extended"],
right_leg_extended=raw["right_leg_extended"],
left_elbow_angle=raw["left_elbow_angle"],
right_elbow_angle=raw["right_elbow_angle"],
left_knee_angle=raw["left_knee_angle"],
right_knee_angle=raw["right_knee_angle"],
feedback=raw["feedback"],
)
result = self._state.update(metrics)
draw_status_overlay(annotated, result)
return annotated, result
def _normalize_timestamp(self, timestamp_ms: int) -> int:
if timestamp_ms <= self._last_timestamp_ms:
timestamp_ms = self._last_timestamp_ms + 1
self._last_timestamp_ms = timestamp_ms
return timestamp_ms
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from __future__ import annotations
import cv2
import numpy as np
from app.exercises.dead_bug.types import Point
def angle(a: Point, b: Point, c: Point) -> float:
ba = np.array([a.x - b.x, a.y - b.y], dtype=np.float32)
bc = np.array([c.x - b.x, c.y - b.y], dtype=np.float32)
denom = float(np.linalg.norm(ba) * np.linalg.norm(bc))
if denom == 0:
return 0.0
cos_value = float(np.dot(ba, bc) / denom)
return float(np.degrees(np.arccos(np.clip(cos_value, -1.0, 1.0))))
def distance(a: Point, b: Point) -> float:
return float(np.hypot(a.x - b.x, a.y - b.y))
def calculate_metrics(
lm: list[Point],
*,
left_shoulder: int,
right_shoulder: int,
left_elbow: int,
right_elbow: int,
left_wrist: int,
right_wrist: int,
left_hip: int,
right_hip: int,
left_knee: int,
right_knee: int,
left_ankle: int,
right_ankle: int,
visibility_threshold: float = 0.45,
) -> dict:
left_elbow_angle = angle(lm[left_shoulder], lm[left_elbow], lm[left_wrist])
right_elbow_angle = angle(lm[right_shoulder], lm[right_elbow], lm[right_wrist])
left_knee_angle = angle(lm[left_hip], lm[left_knee], lm[left_ankle])
right_knee_angle = angle(lm[right_hip], lm[right_knee], lm[right_ankle])
shoulder_width = distance(lm[left_shoulder], lm[right_shoulder])
hip_width = distance(lm[left_hip], lm[right_hip])
scale = max(shoulder_width, hip_width, 0.08)
left_arm_extended = (
left_elbow_angle >= 145
and distance(lm[left_shoulder], lm[left_wrist]) >= scale * 1.15
and lm[left_wrist].y <= lm[left_shoulder].y + scale * 0.35
)
right_arm_extended = (
right_elbow_angle >= 145
and distance(lm[right_shoulder], lm[right_wrist]) >= scale * 1.15
and lm[right_wrist].y <= lm[right_shoulder].y + scale * 0.35
)
left_leg_extended = (
left_knee_angle >= 150
and distance(lm[left_hip], lm[left_ankle]) >= scale * 1.55
and lm[left_ankle].y >= lm[left_knee].y - scale * 0.2
)
right_leg_extended = (
right_knee_angle >= 150
and distance(lm[right_hip], lm[right_ankle]) >= scale * 1.55
and lm[right_ankle].y >= lm[right_knee].y - scale * 0.2
)
feedback: list[str] = []
if left_arm_extended and left_elbow_angle < 160:
feedback.append("Straighten left arm")
if right_arm_extended and right_elbow_angle < 160:
feedback.append("Straighten right arm")
if left_leg_extended and left_knee_angle < 165:
feedback.append("Straighten left leg")
if right_leg_extended and right_knee_angle < 165:
feedback.append("Straighten right leg")
return {
"left_arm_extended": left_arm_extended,
"right_arm_extended": right_arm_extended,
"left_leg_extended": left_leg_extended,
"right_leg_extended": right_leg_extended,
"left_elbow_angle": left_elbow_angle,
"right_elbow_angle": right_elbow_angle,
"left_knee_angle": left_knee_angle,
"right_knee_angle": right_knee_angle,
"scale": scale,
"feedback": feedback,
}
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from __future__ import annotations
from app.exercises.dead_bug.types import DeadBugMetrics, Point
def has_required_visibility(landmarks: list[Point], required_indices: tuple[int, ...], visibility_threshold: float) -> bool:
return all(landmarks[i].visibility >= visibility_threshold for i in required_indices)
def detect_diagonal_extension(metrics: DeadBugMetrics) -> str | None:
if metrics.left_leg_extended and metrics.right_leg_extended:
return None
if metrics.right_leg_extended and metrics.left_arm_extended:
return "left_arm_right_leg"
if metrics.left_leg_extended and metrics.right_arm_extended:
return "right_arm_left_leg"
return None
def is_ready_position(metrics: DeadBugMetrics) -> bool:
knees_bent = metrics.left_knee_angle <= 140 and metrics.right_knee_angle <= 140
legs_not_extended = not metrics.left_leg_extended and not metrics.right_leg_extended
return knees_bent and legs_not_extended and detect_diagonal_extension(metrics) is None
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from __future__ import annotations
from app.exercises.dead_bug.rules import detect_diagonal_extension, is_ready_position
from app.exercises.dead_bug.types import DeadBugMetrics, DeadBugPhase, DeadBugResult
class DeadBugStateMachine:
def __init__(self, *, extension_confirm_frames: int = 4, reset_confirm_frames: int = 3) -> None:
self.extension_confirm_frames = extension_confirm_frames
self.reset_confirm_frames = reset_confirm_frames
self.rep_count = 0
self.phase = DeadBugPhase.READY
self.active_side: str | None = None
self._candidate_side: str | None = None
self._candidate_frames = 0
self._reset_frames = 0
def update(self, metrics: DeadBugMetrics) -> DeadBugResult:
side = detect_diagonal_extension(metrics)
ready = is_ready_position(metrics)
if side is None:
self._candidate_side = None
self._candidate_frames = 0
elif side == self._candidate_side:
self._candidate_frames += 1
else:
self._candidate_side = side
self._candidate_frames = 1
if self.phase in (DeadBugPhase.READY, DeadBugPhase.NO_POSE):
if self._candidate_frames >= self.extension_confirm_frames and side is not None:
self.phase = DeadBugPhase.EXTENDING
self.active_side = side
self._reset_frames = 0
elif self.phase == DeadBugPhase.EXTENDING:
if side == self.active_side:
self.phase = DeadBugPhase.NEED_RESET
elif self.phase == DeadBugPhase.NEED_RESET:
if ready:
self._reset_frames += 1
if self._reset_frames >= self.reset_confirm_frames:
self.rep_count += 1
self.phase = DeadBugPhase.READY
self.active_side = None
self._candidate_side = None
self._candidate_frames = 0
self._reset_frames = 0
else:
self._reset_frames = 0
feedback = list(metrics.feedback)
if side is None and not ready:
feedback.append("Extend opposite arm and leg only")
if ready:
feedback.append("Ready position")
elif side == "left_arm_right_leg":
feedback.append("Left arm + right leg")
elif side == "right_arm_left_leg":
feedback.append("Right arm + left leg")
is_standard = side is not None and not metrics.feedback
return DeadBugResult(
rep_count=self.rep_count,
phase=self.phase,
side=side,
is_standard=is_standard,
feedback=feedback[:3],
metrics=metrics,
)
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from __future__ import annotations
from dataclasses import dataclass
from enum import Enum
class DeadBugPhase(str, Enum):
READY = "ready"
EXTENDING = "extending"
NEED_RESET = "need_reset"
NO_POSE = "no_pose"
@dataclass(frozen=True)
class Point:
x: float
y: float
z: float
visibility: float
@dataclass
class DeadBugMetrics:
left_arm_extended: bool
right_arm_extended: bool
left_leg_extended: bool
right_leg_extended: bool
left_elbow_angle: float
right_elbow_angle: float
left_knee_angle: float
right_knee_angle: float
feedback: list[str]
@dataclass
class DeadBugResult:
rep_count: int
phase: DeadBugPhase
side: str | None
is_standard: bool
feedback: list[str]
metrics: DeadBugMetrics | None